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This paper presents our Facial Action Units (AUs) detection submission to thefifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approachconsists of three main modules: (i) a pre-trained facial representation encoderwhich produce a strong facial representation from each input face image in theinput sequence; (ii) an AU-specific feature generator that specifically learnsa set of AU features from each facial representation; and (iii) aspatio-temporal graph learning module that constructs a spatio-temporal graphrepresentation. This graph representation describes AUs contained in all framesand predicts the occurrence of each AU based on both the modeled spatialinformation within the corresponding face and the learned temporal dynamicsamong frames. The experimental results show that our approach outperformed thebaseline and the spatio-temporal graph representation learning allows our modelto generate the best results among all ablated systems. Our model ranks at the4th place in the AU recognition track at the 5th ABAW Competition. Our code ispublicly available at https://github.com/wzh125/ABAW-5.

Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection